Cuckoo optimization algorithm via Grey Wolf Optimizer for usage in engineering optimization and optimal power flow with renewable energy sources

Optimal Power Flow (OPF) is a critical challenge in electrical engineering, necessitating efficient and resilient optimization techniques for successful power distribution management. This study presents COGWO, an innovative hybrid metaheuristic that integrates the Grey Wolf Optimizer (GWO) with the...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Scientific reports Ročník 15; číslo 1; s. 37629 - 37
Hlavní autoři: Abbassi, Rabeh, Trojovský, Pavel, Mansor, Zulkefli, Trojovská, Eva, Kchaou, Mourad, Zuščák, Tomáš, Jerbi, Houssem, Naveen, Palanichamy
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 28.10.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/4077
639/705
Algorithms
Alternative energy sources
Archives & records
COGWO
Convergence
Cuckoo optimization algorithm
Electrical engineering
Energy consumption
Engineering optimization
Genetic algorithms
Grey Wolf Optimizer
Humanities and Social Sciences
Multi-objective OPF
multidisciplinary
Optimization algorithms
Optimization techniques
Parameter estimation
Renewable energy sources
Renewable energy sources (RESs)
Renewable resources
Science
Science (multidisciplinary)
Cuckoo optimization algorithm
Multi-objective OPF
Grey Wolf Optimizer
Renewable energy sources (RESs)
COGWO
Engineering optimization
ISSN:2045-2322, 2045-2322
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Optimal Power Flow (OPF) is a critical challenge in electrical engineering, necessitating efficient and resilient optimization techniques for successful power distribution management. This study presents COGWO, an innovative hybrid metaheuristic that integrates the Grey Wolf Optimizer (GWO) with the Cuckoo Optimization Algorithm (COA) to enhance convergence quality and solution resilience. Before its implementation in OPF issues, the suggested technique was thoroughly verified against standard engineering problems in CEC2020, continuously surpassing several state-of-the-art methods. Subsequently, COGWO was utilized to tackle OPF issues in the IEEE 30-bus and 118-bus systems, accounting for the fluctuation of renewable energy sources (RESs), such as wind and solar, in conjunction with traditional power network configurations. The method exhibits an optimal balance between exploration and exploitation, successfully minimizing fuel costs, power loss, voltage variation, and emissions, even in the presence of intricate non-convex and non-smooth optimization functions. A comparative examination with COA, GWO, and other modern metaheuristics demonstrates the advantage of COGWO in attaining high-quality global solutions characterized by improved solution stability and convergence speed. When it comes to optimizing power systems on a grand scale, COGWO is an attractive solution due to its computational efficiency, flexibility, and resilience.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-21515-3